Multi-sensor fusion approach with fault detection and exclusion based on the Kullback–Leibler Divergence: Application on collaborative multi-robot system

Hage, Joelle Al; Najjar, Maan E. El; Pomorski, Denis

doi:10.1016/j.inffus.2017.01.005

Cited by 98 publications

(47 citation statements)

References 22 publications

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“…This informational metric can be considered as the expected Log Likelihood Ratio (LLR). It takes in consideration the Mahalanobis distance and the Bergmann divergence that assess the orientation and the compactness of the measurements distributions represented respectively by the trace and the determinant of their covariance matrices [7,28].…”

Section: ) Fault Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Toward High Integrity Personal Localization System Based on Informational Formalism

Daher

Hage

Najjar

et al. 2019

IEEE Trans. Instrum. Meas.

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Section: ) Fault Detectionmentioning

confidence: 99%

“…The KLD between the data distribution obtained in the predicted step of the EIF (g(k/k −1)) and the distribution obtained in the corrected step (g(k/k)) is called the Global Kullback-Leibler Divergence (GKLD) [7,28,29], and it has the following form:…”

Section: ) Fault Detectionmentioning

confidence: 99%

Toward High Integrity Personal Localization System Based on Informational Formalism

Daher

Hage

Najjar

et al. 2019

IEEE Trans. Instrum. Meas.

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“…Some authors have also proposed Extended KF (EKF) [ 129 , 130 ] and Unscented KF (UKF) [ 131 ] based approaches with the advantage of inconsistencies detection in non-linear systems. Multisensor data fusion with fault detection and removal based on Kullback-Leibler Divergence (KLD) for multi-robot system was proposed in Reference [ 132 ]. The method computes the KLD between the a priori and posteriori distributions of the Information Filter (IF) and uses Kullback-Leibler Criterion (KLC) thresholding to detect and remove the spurious sensor data.…”

Section: Fusion Of Inconsistent and Spurious Datamentioning

confidence: 99%

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

Bakr

Lee

2017

Sensors

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The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted.

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“…To improve the robustness of the robot team, fault detection and isolation (FDI) [ 13 , 14 , 15 ] are needed. In [ 16 ], an FDI algorithm was applied in CCL. However, the FDI algorithms cited above are designed for CCL and are not applicable in DCL due to the limited sensor information available.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Cooperative Localization with Fault Detection and Isolation in Robot Teams

Zhang

2018

Sensors

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Robot localization, particularly multirobot localization, is an important task for multirobot teams. In this paper, a decentralized cooperative localization (DCL) algorithm with fault detection and isolation is proposed to estimate the positions of robots in mobile robot teams. To calculate the interestimate correlations in a distributed manner, the split covariance intersection filter (SCIF) is applied in the algorithm. Based on the split covariance intersection filter cooperative localization (SCIFCL) algorithm, we adopt fault detection and isolation (FDI) to improve the robustness and accuracy of the DCL results. In the proposed algorithm, the signature matrix of the original FDI algorithm is modified for application to DCL. A simulation-based comparative study is conducted to demonstrate the effectiveness of the proposed algorithm.

show abstract

Multi-sensor fusion approach with fault detection and exclusion based on the Kullback–Leibler Divergence: Application on collaborative multi-robot system

Cited by 98 publications

References 22 publications

Toward High Integrity Personal Localization System Based on Informational Formalism

Toward High Integrity Personal Localization System Based on Informational Formalism

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

Decentralized Cooperative Localization with Fault Detection and Isolation in Robot Teams

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